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1. How does sustaining and interleaving visual scaffolding help learners? a classroom study with an intelligent tutoring system

   Nagashima, T.; Ling, E.; Zheng, B.; Bartel, A. N.; Silla, E. M.; Vest, N. A.; Alibali, M. W.; Aleven, V. (July 2022, Proceedings of the 44th Annual Conference of the Cognitive Science Society)

   Culbertson, J.; Perfors, A.; Rabagliati, H.; Ramenzoni, V. (Ed.)

   Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how sustaining or interleaving visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63 middle-school students in which we tested whether sustaining or interleaving a particular form of visual scaffolding, called anticipatory diagrammatic self-explanation in an Intelligent Tutoring System, helps students’ learning and performance in the domain of early algebra. Sustaining visual scaffolding during problem solving helped students solve problems efficiently with no negative effects on learning. However, in-depth log data analyses suggest that interleaving visual scaffolding allowed students to practice important skills that may help them in later phases of algebra learning. This paper extends scientific understanding that sustaining visual scaffold does not over-scaffold student learning in the early phase of skill acquisition in algebra. 

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2. Scaffolded self-explanation with visual representations promotes efficient learning in early algebra

   https://doi.org/10.31219/osf.io/sbwfj  

   Nagashima, T.; Bartel, A. N.; Tseng, S.; Vest, N. A.; Silla, E. M.; Alibali, M. W.; Aleven, V. (January 2021, Proceedings of the 43rd Annual Conference of the Cognitive Science Society)

   Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K. (Ed.)

   Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self-explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance and the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance. 

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3. Scaffolded self-explanation with visual representations promotes efficient learning in early algebra.

   https://doi.org/https://doi.org/10.31219/osf.io/sbwfj  

   Nagashima, T.; Bartel, A. N.; Tseng, S.; Vest, N. A.; Silla, E. M.; Alibali, M. W.; Aleven, V. (January 2021, Proceedings of the 43rd Annual Conference of the Cognitive Science Society)

   Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K. (Ed.)

   Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self-explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance and the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance. 

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4. Understanding the Effects of Using Parsons Problems to Scaffold Code Writing for Students with Varying CS Self-Efficacy Levels

   https://doi.org/10.1145/3631802.3631832  

   Hou, Xinying; Ericson, Barbara Jane; Wang, Xu (November 2023, Proceedings of the 23rd Koli Calling International Conference on Computing Education Research (Koli Calling))

   Introductory programming courses aim to teach students to write code independently. However, transitioning from studying worked examples to generating their own code is often difficult and frustrating for students, especially those with lower CS self-efficacy in general. Therefore, we investigated the impact of using Parsons problems as a code-writing scaffold for students with varying levels of CS self-efficacy. Parsons problems are programming tasks where students arrange mixed-up code blocks in the correct order. We conducted a between-subjects study with undergraduate students (N=89) on a topic where students have limited code-writing expertise. Students were randomly assigned to one of two conditions. Students in one condition practiced writing code without any scaffolding, while students in the other condition were provided with scaffolding in the form of an equivalent Parsons problem. We found that, for students with low CS self-efficacy levels, those who received scaffolding achieved significantly higher practice performance and in-practice problem-solving efficiency compared to those without any scaffolding. Furthermore, when given Parsons problems as scaffolding during practice, students with lower CS selfefficacy were more likely to solve them. In addition, students with higher pre-practice knowledge on the topic were more likely to effectively use the Parsons scaffolding. This study provides evidence for the benefits of using Parsons problems to scaffold students’ write-code activities. It also has implications for optimizing the Parsons scaffolding experience for students, including providing personalized and adaptive Parsons problems based on the student’s current problem-solving status. 

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5. Learning to Think like a Neuron in Middle School

   https://doi.org/10.1609/aaai.v39i28.35195  

   Touretzky, David; Gardner-McCune, Christina; Hanna, Will; Chen, Angela; Pawar, Neel (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Neuron Sandbox is a browser-based tool that helps middle school students grasp basic principles of neural computation. It simulates a linear threshold unit applied to binary decision problems, which students solve by adjusting the unit's threshold and/or weights. Although Neuron Sandbox provides extensive visualization aids, solving these problems is challenging for students who have not yet been exposed to algebra. We collected survey, video, and worksheet data from 21 seventh grade students in two sections of an AI elective, taught by the same teacher, that used Neuron Sandbox. We present a scaffolding strategy that proved effective at guiding these students to achieve mastery of these problems. While the amount of scaffolding required was more than we originally anticipated, by the end of the exercise students understood the computation that linear threshold units perform and were able to generalize their understanding of the worksheet’s solve for threshold strategy to also solve for weights. 

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